Mechanism◆Video · 4:16 — 5:03

The unique bottleneck in general-purpose robotics is the lack of data for training and evaluation, which can be solved by a 'real-to-sim-to-real' pipeline that aligns digital worlds with physical environments so that simulation data replaces real-world data at scale.

Yunzhu Li describes the data bottleneck in robotics and explains Scenix's approach of mapping real environments into aligned digital worlds for scalable training and evaluation. ✦ AI generated

Yunzhu Li · a16z Podcast · 2026-07-28 · original ↗

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“So, Yunzhu, you're the co-founder of Scenix. So, maybe provide everyone with a quick overview of your background and what Scenix does.”

Throughout my career, my goal has been very simple: trying to help the robots better perceive and interact with the physical world. So I'm a very practical person. I want my robot to work in the real physical environments. So for Scenix the unique opportunity we see is that there has been a lot of bottlenecks right now we see faced by the developments of general purpose robots especially around training and also around evaluations. So as we are developing what we call a real to sim to real pipeline. We want to map the real environments into the digital world that has the best alignments with the real environments. By alignments we mean that whatever happens in the digital world is also going to happen in the real environments such that we can replace all the data, all the evaluation we need in the real environment by using the data that can generate at a scalable way in our digital world.

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4:16person. I want my robot to work in the real physical environments. >> So for cynics the unique opportunity we see is that there has been a lot of like a bottlenecks. Right now we see faced by the developments of general purpose robots especially around training and also around evaluations. So as we are developing what we call a real to sim to real pipeline. Okay. We want to map the

4:40real environments into the digital world that has the best alignments with the real environments. By alignments we mean that whatever happens in the digital world is also going to happen in the real environments >> such that we can replace all the data all the evaluation we need in the real environment by using the data that can generate at a scalable way in our digital world. So that is how everything

5:03started in Synex. We put together a very very strong and best teams around robotics, robot learning and also simulation and rendering trying to build this realtom real stack to solve some of the key bottlenecks. >> It's amazing that you two work together. >> Yeah. And uh there is a funny story here because you would think because we worked together he was my amazing postto we've been talking about this and world

5:27lab um um integration for a long time. It's actually not true. They came into World Wars as a customer. >> Really? >> When we when we released the first version of our generative model called Marble last winter around uh November, December, >> Cynics just signed up. >> No kidding. AS A CUSTOMER. >> YES. [laughter] And I didn't even know what it was. And then I realized this is Vindrew's

▦ Scenix ◉ Yunzhu Li

Around this claim

Mechanism · 1

Spatial intelligence—the ability to generate, understand, reason with, and interact with physical or virtual spaces—is the next frontier of AI, and building large world models is the means to achieve it.Fei-Fei Li · a16z Podcast · conf 70%

Evidence · 1

Mapping real environments into aligned digital worlds enables replacing all real-world robotics data with scalable digital data, solving the key training and evaluation bottlenecks in general-purpose robotics.Yinuo · a16z Podcast · conf 90%

Extends · 2

The fundamental bottleneck in robotics is the lack of data for training and evaluation — unlike language models where data is abundant on the internet, robotics requires a real-to-sim-to-real pipeline to unlock scaling laws.Yunzhu Li · a16z Podcast · conf 90%The lack of data in training and evaluation is the most profound problem in robotics, fundamentally different from language models where data is abundant, and unlocking scaling laws for robotics depends on solving it.Fei-Fei Li · a16z Podcast · conf 85%

This moment responds to

supports → The real-to-sim-to-real pipeline maps real environments into aligned digital worlds to replace costly real-world data collection with scalable simulation data for robotics training and evaluation.Yunzhu Li · a16z Podcast supports → Simulation provides two unique benefits that real-world data cannot: systematic coverage for reliability (controllable variation of all state-space parameters) and accelerated data generation for efficiency (faster-than-real-time training).Yunzhu Li · a16z Podcast explains mechanism → The key role simulation plays that real-world data cannot is counterfactual reasoning—playing out events that haven't happened or cannot happen—which is essential because robotics cannot possibly collect enough real-world data for all scenarios.Fei-Fei Li · a16z Podcast explains mechanism → Simulation provides two distinct benefits for robotics: reliability through systematic randomization covering the full state space, and efficiency through controllable speed-up of robot behaviors that is impossible in the physical world.Yunzhu Li · a16z Podcast extends → A foundation model for robotics will be an omni-model that takes multimodal input—including actions as input (a forward simulator predicting environment changes) or actions as output (a policy model)—and serves as a backbone fine-tuned for specific robotic applications.Fei-Fei Li and Yunzhu Li · a16z Podcast provides context → Human-level power efficiency in robotics will take a very long time to achieve because a working robot is always a system problem integrating hardware, software, and countless details, and we should take a measured, realistic approach rather than over-promising.Yunzhu Li and Fei-Fei Li · a16z Podcast